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The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

The purpose of this study was to design and iteratively improve the quality of survey-based measures of three non-cognitive constructs for Grade 5-6 students, keeping in mind information needs of users in education reform contexts. The constructs are: Mathematics-related Self-Efficacy, Self-Concept, and Anxiety (M-SE, M-SC, and M-ANX).

The authors applied a multi-stage, iterative and user-centered approach to design and validate the measures, using several psychometric techniques and three data samples. They evaluated the utility of student-level scores and aggregated, classroom-level means.

At both student and classroom levels, replicated evidence supported theoretically-grounded validity arguments on information produced by four of five scales tapping M-SC, M-ANX and M-SE. The evidence confirmed a second order, two-factor structure for M-SC, representing positive math affect and perceived competence, and a one factor structure for M-ANX representing negative math affect. Consistent with the literature, these served as precursors to a perceived confidence factor of M-SE which, in turn, positively influenced mathematics achievement scores, off-setting negative effects of M-ANX. Research is continuing on a self-regulatory efficacy factor of M-SE, which yielded mixed results.

The survey scales are in line with current reform policies in the United States calling for schools to monitor changes in cognitive and non-cognitive domains of student development. Validated scales could be useful in serving information needs of teachers, decision-makers and researchers in similar school-based contexts.

This study demonstrates a comprehensive, user-centered methodology for designing and validating construct measures, departing from purely psychometric traditions of scale development.

Taking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives – the initial Wuhan lockdown, local COVID-19 cases and local lockdowns.

Using Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings.

The authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market.

The empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown.

This research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry.

With the rapid development of sharing economy, travelers are facing choices between conventional hotels and the peer-to-peer sharing accommodation in urban tourism. The purpose of this study is to examine how travelers form their preferences in such choice situations and whether/how their preference formation mode would change with the COVID-19 pandemic.

A relative preference model was constructed and estimated for both domestic and outbound tourists, based on two waves of survey data collected before and after the COVID-19. The results of this study were compared to derive the evolution of preference formation patterns.

A set of 15 key value attributes and personal traits was identified, together with their differential effects with the pandemic. Their divergent effects between domestic and outbound trips were also delineated. Based on these findings, the competitive edges and advantageous market profiles were depicted for both hotel and sharing accommodation sectors.

This study contributes to the knowledge of tourists’ preference between accommodation types and adds empirical evidences to the impact of the pandemic on tourist behavior patterns. Both hotel and sharing accommodation practitioners can benefit from the findings to enhance their competitiveness.

The purpose of this paper is to benchmark the Indian mobile telecommunication service providers for relative efficiencies. In this paper, a method for benchmarking performance of mobile telecom utilities based on data envelopment analysis (DEA) is presented. The paper discusses some concepts between quality performance and benchmarking and the results include performance efficiency and sensitivity‐based classification of utilities. Also, peer‐to‐peer comparison of inefficient with efficient utilities is provided. Based on these results, inefficient utilities can develop strategic plans to improve performance.

The authors use DEA to measure comparative efficiencies of mobile telecom companies and two different DEA models, CCR and BCC, were applied to evaluate the relative efficiency of mobile telecom operators in India. Sensitivity‐based classification of utilities is carried out by removing one or more inputs or outputs from the base model to construct a new DEA model. Comparisons of DEA efficiencies from the base model with the structurally perturbed models show the impact on efficiency. Data include annual and quarterly reports showing various quality parameters.

DEA is used to derive the benchmarks based on the comparison of the 126 utilities which include public sector undertaking (PSU) operators (MTNL and BSNL) and private operators of the Indian mobile telecommunication sector. The result includes performance efficiency and peer‐to‐peer comparison of inefficient utilities with efficient utilities. Based on these results, inefficient utilities can develop strategic plans to improve their performance. Sensitivity analysis, based on removal of one or more variables from the base model to determine changes in DEA efficiencies is done for selecting the strength of parameters of utilities for performance improvement.

Benchmarking of service utilities in the telecom sector is virtually non‐existent at the national level in India. This research identifies the different variables and then a model is prepared for benchmarking of the service providers in India. Based on the efficiency analysis, benchmarks can be set, and utility efficiency scores can be obtained based on the set benchmarks. These scores can help develop a strategic plan for mitigating the factors that contribute to the system inefficiencies.

This paper is one of the few published studies that benchmark the performance of mobile telecom services in India. This research promises to be amongst the first of the works carried out taking specific parameters of mobile telecom utilities of India.

This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore.

This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research.

This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods.

Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies.

This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

The sharing economy is a socio-economic system in which individuals acquire and distribute goods and services among each other for free or for compensation through internet platforms. The sharing economy has attracted the interest of the academic community, which examined the phenomenon from the economic, social and technological perspectives. The paper aims to discuss this issue.

Given the lack of an overarching analysis of the sharing economy, this paper employs a quantitative content analysis approach to explore and synthesise relevant findings to facilitate the understanding of this emerging phenomenon.

The paper identified and grouped findings under four themes, namely: collaborative consumption practices, resources, drivers of user engagement and impacts, each of which is discussed in relation to the three main themes, aiming to compare findings and then put forward an agenda for further research.

The paper offers a balanced analysis of the building blocks of the sharing economy, to identify emerging themes within each stream, to discuss any contextual differences from a multi-stakeholder perspective and to propose directions for future studies.

Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.

Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.

The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.

The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

The purpose of this paper is to evaluate performance of Indian telecom service providers through a benchmarking study of their operational efficiency and service delivery effectiveness. The paper also carries out a peer-to-peer comparison and identifies-specific areas of improvement for different service providers to attain sustainable growth and profitability.

A two stage data envelopment analysis (DEA) model was used to compare the performances of the service providers. The first stage represents how efficiently a unit is able to use its infrastructure and resources to generate better quality services. The second stage captures how well the company is able to communicate and deliver these services to the customer.

The results of the study support the applicability of the two stage DEA for comparing the performances of the telecom service providers as they are in line with the financial performance indicators and brand ranking. It is observed that those companies which score high on both operational efficiency and service delivery effectiveness have achieved superior profitability.

This study has been carried out at a pan-India level and hence does not take into account circle level or local performance which varies significantly for most service providers. Besides, this the analysis was constrained by limited data in the public domain, which necessitated estimations and extrapolations for some variables of few service providers.

The study has helped to provide inputs for the Indian telecom companies for potential performance improvements by providing a comparative analysis of their operational efficiency and service delivery effectiveness. It has enabled to derive deeper insights on potential target areas for managerial attention that could be translated into implementable actions. The benchmarking analysis has also helped to understand whether the current performance of the service provider is sustainable, unprofitable or ephemeral.

This paper goes beyond the traditional benchmarking studies of Indian telecom service providers introducing a two stage DEA model to understand the operational efficiency as well as the service delivery effectiveness. The study has helped to derive valuable academic and practical insights on the issue of performance measurement of the Indian telecom service providers.

The purpose of this paper is to propose a short-term renters’ preference profile for peer-to-peer (P2P) accommodation selection in the housing market in Kuala Lumpur, Malaysia. The emergence of P2P accommodation was initiated by the concept of a sharing economy, allowing the peer provider to share personal properties with the peer end-users. As the housing market has become more competitive, understanding the preference profile among short-term renters is advantageous to offer a matching marketing mix to the targeted customers.

This study uses qualitative method associated with thematic analysis. A total of 10,509 reviews on Airbnb were gathered from 377 properties in Kuala Lumpur Federal Territory from 2013 to 2017. Thematic analysis assisted by NVivo software was applied to analyse the empirical data. In total, 14 attributes were identified which could be categorised into 5Ps. The concept of 5Ps adopted from modern marketing is commonly used to categorise the strategies in the marketing process. In the current context, 5Ps is used to categorise the preference of the short-term renter in their selection of P2P accommodation including product, price, promotion, process and people.

This paper has developed a preference profile for P2P accommodation selection in Malaysia’s housing market. In total, 14 attributes were identified and categorised into the 5Ps of marketing mix. The results showed that the majority of short-term renters were concerned mostly with product (73.2 per cent), followed by people (14.5 per cent), process (10.1 per cent), price (2.0 per cent) and promotion (0.2 per cent).

This study contributes to existing literature with a novel case in Malaysia’s housing market by identifying the short-term renter’s preference in the P2P accommodation selection in the Malaysian housing market, specifically in Kuala Lumpur. The preference profile provides guidance for property owners and developers in the housing market to offer the right product in enhancing the marketability and rentability of the property.